创建具有多个数据框的 pandas 数据框 [英] Create pandas dataframe with multiple dataframes

问题描述

我有一个这样的csv文件:

I've a csv file like this:

Fruit_Type;Fruit_Color;Fruit_Description
Apple;Green,Red,Yellow;Just an apple
Banana;Green,Yellow;Just a Banana
Orange;Red,Yellow;Just an Orange
Grape;;Just a Grape

(注意:单元格内有逗号，颜色类型编号是可变的，最多可以有三种颜色)

( Note: There're commas inside of a cell and the colors type number is variable with a maximum of three different colors )

我想要的结果是:

水果类型;水果颜色;水果描述

Fruit_Type;Fruit_Color;Fruit_Description

Apple;Green;0;0;Just an apple
Apple;0;Red;0;Just an apple
Apple;0;0;Yellow;Just an apple
Banana;Green;0;0;Just a Banana
Banana;0;Red;0;Just a Banana
Banana;0;0;Yellow;Just a Banana
Orange;Green;0;0;Just an Orange
Orange;0;Red;0;Just an Orange
Orange;0;0;Yellow;Just an Orange
Grape;0;0;0;Just a Grape
Grape;0;0;0;Just a Grape
Grape;0;0;0;Just a Grape

我想将数据帧Fruit_Color列分为3列，这些颜色上的0值不存在.

I want to split the dataframe Fruit_Color column into 3 columns with a 0 value on those colors what aren't present.

我试图像这样转换数据框信息数据框以获取包含一些字符串的行:

I've tryed to convert the dataframe info dataframes like this to get the lines what contais some string:

test.py

#load the csv data into dataframe
data = pd.read_csv(open('test.py','rb'),delimiter=';',encoding='utf-8')

#detect the rows where're the color
Green = data.loc[data['Fruit_Color'].str.contains('Green', case=True)]
Red = data.loc[data['Fruit_Color'].str.contains('Red', case=True)]
Yellow = data.loc[data['Fruit_Color'].str.contains('Yellow', case=True)]

有了这些行，我就包含了特定的颜色，但是我不知道如何用这些数据帧制作连接的数据框，我又怎么知道那些行没有像葡萄这样的颜色?

With that i've the rows what contains specific color but i dont know how i can make the joined dataframe with those dataframes and also how can i know those rows what doesn't have any color like the Grape ?

谢谢.

推荐答案

我建议使用

I suggest use str.get_dummies:

df = df.join(df.pop('Fruit_Color').str.get_dummies(','))
print (df)
  Fruit_Type Fruit_Description  Green  Red  Yellow
0      Apple     Just an apple      1    1       1
1     Banana     Just a Banana      1    0       1
2     Orange    Just an Orange      0    1       1
3      Grape      Just a Grape      0    0       0

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